User defined scaled mass defect plot with filtering and labeling

ABSTRACT

A method of determining mass defect plots with user-defined mass scaling, filtering, and labeling in a mass spectrometer is described. An implementation of the method comprises, (i) generating a mass defect plot from the data, (ii) filtering all ions in the mass defect plot that do not have an associated isotopologue ion, (iii) selecting an unidentified ion, (iv) determining an isotope pattern of the unidentified ion, (v) identifying one or more elements indicated by the isotope pattern for the unidentified ion; (vi) searching formulas containing one or more elements indicated by the isotope pattern for the unidentified ion, (vii) determining a chemical formula of the identified ion, and (viii) displaying the chemical formulas for the unidentified ion on a screen.

TECHNICAL FIELD

This disclosure relates to generating mass defect plots withuser-defined mass scaling, filtering, and labeling.

BACKGROUND

Mass spectrometry (MS) is an analytical technique that can be used fordetermining the mass of an ion, which may be used to interpretinformation about a compound such as elucidating the chemical structuresof molecules, including small metabolites and other chemical compounds.Mass spectrometry generally includes ionizing chemical compounds togenerate charged molecules or molecule fragments and then measuringtheir mass-to-charge ratios. In a typical MS procedure, a sample loadedonto a mass spectrometer undergoes vaporization and the components ofthe sample are ionized to form charged particles (ions). The ions aretypically accelerated by an electric field for computation of themass-to-charge ratio (m/z) of the particles based on the details ofmotion of the ions as they move through electromagnetic fields. The ionsmay be sorted by a mass analyzer according to their mass-to-charge ratio(m/z) and detected measuring the value of an indicator quantity andproviding data for calculating the abundances of each ion present. Thecalculated mass of each ion may change or drift during operation of themass spectrometer, due to various factors.

Every isotope has a defined mass defect depending on its relativenuclear binding energy to carbon-12. Each nuclide has a different massdefect and every molecule of a specific elemental composition will havethe mass uniquely characteristic of that elemental composition. The massdefect is determined by the difference between the exact mass of theisotope in question and the normal integer mass of the isotope inquestion. The specific mass defect may be used to assist in identifyingthe exact chemical formula. This application presents a method forfiltering and labeling of specific isotopes and chemical compounds bymass defect based on accurate mass determination.

SUMMARY

One aspect of the disclosure provides a method of constructing afiltered mass defect plot based on accurate mass data acquired from amass spectrometer. In an implementation, the filtered mass defect plotmay be a halogen filtered mass defect plot. The method includesgenerating a mass defect plot from data obtained from the massspectrometer, filtering all ions in the mass defect plot that do nothave an associated confirmatory isotopologue (e.g. an M+2 or M−2 ion),selecting an unidentified ion, and determining an isotope pattern of theunidentified ion. The method further includes identifying one or moreelements indicated by the unidentified ion, searching formulascontaining one or more elements indicated by the isotope pattern for theunidentified ion, determining a chemical formula of the unidentifiedion, and displaying the chemical formula for the unidentified ion on ascreen.

Implementations of the disclosure may include one or more of thefollowing optional features. In some implementations, the methodincludes receiving a user selection of an ion, displaying the selectedion as an extracted ion chromatogram±mass tolerance, and identifying oneor more chromatographic peak(s) corresponding to the extracted ionchromatogram±mass tolerance. The method may also include identifyinghomologous series and RDBE related species related to the unidentifiedion. The homologous series may further include chlorine and or bromine.The RDBE related species may include deuterium and/or hydrogen.

In some examples, the data is raw data from the mass spectrometer. Thedata may be deconvoluted data from the mass spectrometer. The method mayalso include labeling the chemical formulas for the unidentified ion onthe screen. The method may further include assigning a color to theunidentified ion on the screen.

In some implementations, the mass defect plot is a chlorine substitutedfor hydrogen (Cl—H) mass defect plot. The mass defect plot may also be abromine substituted for hydrogen (Br—H) mass defect plot. Filtering allions in the mass defect plot that do not have an associated confirmatoryisotopologue (e.g. an M+2 or M−2) ion may further include filtering themass defect with a specific tolerance and relative abundance. Filteringall ions in the mass defect plot that do not have an associated M+2 orM−2 ion may also include filtering all ions that do not match Br_(x)isotope pattern, where x is an integer between 1 and 15 inclusive.Filtering all ions in the mass defect plot that do not have anassociated M+2 or M−2 ion may also include filtering all ions that donot match Cl_(y) isotope pattern, where y is an integer between 1 and 15inclusive. In some examples, filtering all ions in the mass defect plotthat do not have an associated M+2 or M−2 ion includes filtering allions that do not match Br_(x)Cl_(y) isotope pattern. Filtering all ionsin the mass defect plot that do not have an associated confirmatoryisotopologue may further include filtering by determining a spacingtolerance.

The spacing tolerance may be based on a static m/z distance between thefirst signal and the second signal. The spacing tolerance may be basedon a statistical m/z confidence interval determined from the number ofions in the first signal and the number of ions in the second signal.The spacing tolerance may be based on a statistical m/z confidenceinterval may be approximately 2.8. The spacing tolerance may be limitedby user input.

In some examples, filtering all ions in the mass defect plot that do nothave an associated confirmatory isotopologue includes filtering byrelative abundance. The relative abundance may be determined for M+1signals. Determining the relative abundance for M+1 signals may furtherinclude determining a maximum predicted count of an M+1 element based onan intensity of a putative M+1 signal, an intensity of a putativemonoisotopic signal, and a terrestrial natural abundance of the M+1element. The M+1 element may be carbon, nitrogen, silicon, or any otherelement with a naturally occurring M+1 isotope.

Determining the relative abundance for M+2 signals may includedetermining a maximum predicted count of an M+2 element based on anintensity of a putative monoisotopic signal, an intensity of a putativeM+2 signal, and a terrestrial natural abundance of the M+2 element. Insome examples, determining the relative abundance for M+2 signalsfurther includes determining a maximum predicted count of an M+2 elementbased on an intensity of a putative monoisotopic signal, an intensity ofa putative M+2 signal, an intensity of a putative M+4 signal and aterrestrial natural abundance of the M+2 element. Determining therelative abundance for M+2 signals may also include determining amaximum predicted count of an M+2 element based on an intensity of aputative monoisotopic signal, an intensity of a putative M+4 signal, anintensity of a putative M+6 signal and a terrestrial natural abundanceof the M+2 element. Determining the relative abundance for M+2 signalsmay further include determining a maximum predicted count of an M+2element based on an intensity of a putative monoisotopic signal, anintensity of a putative M+6 signal, an intensity of a putative M+8signal and a terrestrial natural abundance of the M+2 element. In someimplementations, determining the relative abundance for M+2 signalsincludes determining if one or more analytes contain both chlorine andbromine, and if the one or more analytes contain both chlorine andbromine, determining a maximum predicted count of an M+2 element basedon the terrestrial natural abundance of ³⁷Cl, and the terrestrialnatural abundance of ⁸¹Br.

Another aspect of the disclosure provides a method of constructing afiltered mass defect plot based on accurate mass data acquired from amass spectrometer. The device includes a display, data processinghardware in communication with the display, and memory hardware incommunication with the data processing hardware. The memory hardwarestores instructions, that when executed on the data processing hardwarecause the data processing hardware to perform operations. The operationsinclude generating a mass defect plot from data obtained from a massspectrometer, filtering all ions in the mass defect plot that do nothave an associated confirmatory isotopologue, selecting an unidentifiedion, and determining an isotope pattern for an isotopic cluster of theunidentified ion. The operations also include identifying one or moreelements contained within the isotope pattern for the unidentified ion,searching formulas containing one or more elements identified by theisotope pattern for the unidentified ion, determining a chemical formulaof the isotopic cluster related to the unidentified ion, and displayingthe chemical formulas for the unidentified ion on a display.

This aspect may include one or more of the following optional features.The operations may include receiving a user selection of an ion,displaying the selected ion as an extracted ion chromatogram±masstolerance, and identifying one or more chromatographic peak(s)corresponding to the extracted ion chromatogram±mass tolerance. Theoperations may further include identifying homologous series and RDBErelated species related to the unidentified ion. The homologous seriesmay include chlorine and/or bromine. The RDBE related species mayinclude deuterium and/or hydrogen.

In some examples, the data is raw data from a mass spectrometer. Thedata may be deconvoluted data from a mass spectrometer. The operationsmay include labeling the chemical formulas for the unidentified ion onthe display. The operations may also include assigning a color to theunidentified ion on the display.

In some implementations, the mass defect plot is a chlorine substitutedfor hydrogen (Cl—H) mass defect plot. The mass defect plot may also be abromine substituted for hydrogen (Br—H) mass defect plot. Filtering allions in the mass defect plot that do not have an associated confirmatoryisotopologue (e.g. an M+2 or M−2) ion may further include filtering themass defect with a specific tolerance and relative abundance. Filteringall ions in the mass defect plot that do not have an associated M+2 orM−2 ion may also include filtering all ions that do not match Br_(x)isotope pattern, where x is an integer between 1 and 15 inclusive.Filtering all ions in the mass defect plot that do not have anassociated M+2 or M−2 ion may also include filtering all ions that donot match Cl_(y) isotope pattern, where y is an integer between 1 and 15inclusive. In some examples, filtering all ions in the mass defect plotthat do not have an associated M+2 or M−2 ion includes filtering allions that do not match Br_(x)Cl_(y) isotope pattern. Filtering all ionsin the mass defect plot that do not have an associated confirmatoryisotopologue may further include filtering by determining a spacingtolerance.

The spacing tolerance may be based on a static m/z distance between thefirst signal and the second signal. The spacing tolerance may be basedon a statistical m/z confidence interval determined from the number ofions in the first signal and the number of ions in the second signal.The spacing tolerance may be based on a statistical m/z confidenceinterval may be approximately 2.8. The spacing tolerance may be limitedby user input.

In some examples, filtering all ions in the mass defect plot that do nothave an associated confirmatory isotopologue includes filtering byrelative abundance. The relative abundance may be determined for M+1signals. Determining the relative abundance for M+1 signals may furtherinclude determining a maximum predicted count of an M+1 element based onan intensity of a putative M+1 signal, an intensity of a putativemonoisotopic signal, and a terrestrial natural abundance of the M+1element. The M+1 element may be carbon, nitrogen, silicon, or any otherelement with a naturally occurring M+1 isotope.

The relative abundance may also be determined for M+2 signals. Theoperation determining the relative abundance for M+2 signals mayinclude, determining a maximum predicted count of an M+2 element basedon an intensity of a putative monoisotopic signal, an intensity of aputative M+2 signal and a terrestrial natural abundance of the M+2element. The operation determining the relative abundance for M+2signals may also include determining a maximum predicted count of an M+2element based on an intensity of a putative monoisotopic signal, anintensity of a putative M+2 signal, an intensity of a putative M+4signal and a terrestrial natural abundance of the M+2 element. Theoperation determining the relative abundance for M+2 signals may furtherinclude, determining a maximum predicted count of an M+2 element basedon an intensity of a putative monoisotopic signal, an intensity of aputative M+4 signal, an intensity of a putative M+6 signal and aterrestrial natural abundance of the M+2 element. In some examples, theoperation determining the relative abundance for M+2 signals includes,determining a maximum predicted count of an M+2 element based on anintensity of a putative monoisotopic signal, an intensity of a putativeM+6 signal, an intensity of a putative M+8 signal and a terrestrialnatural abundance of the M+2 element. The operation determining therelative abundance for M+2 signals may further include determining ifone or more analytes contain both chlorine and bromine, and if theanalytes contain both chlorine and bromine, determining a maximumpredicted count of an M+2 element based on the terrestrial naturalabundance of ³⁷Cl, and the terrestrial natural abundance of ⁸¹Br.

The details of one or more implementations of the disclosure are setforth in the accompanying drawings and the description below. Otheraspects, features, and advantages will be apparent from the descriptionand drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic view an exemplary time-of-flight mass spectrometer(TOF-MS) mass analyzer.

FIG. 2 is a schematic view a TOF-MS and sample introduction gaschromatograph system.

FIG. 3 provides an exemplary arrangement of operations for labeling amass defect plot (MDP) with halogen filtering.

FIG. 4 provides an example method 400 to determine spacing signals andrelative isotopic abundance.

FIG. 5 shows m/z spacing distribution of Br isotopologues amongstmultiple spectra of the molecular ion of C₄Br₄S (nominal m/z 400)

FIG. 6 shows an example extracted ion chromatogram and total ionchromatogram based on the data from the TOF-MS.

FIG. 7 shows an example graph of mass defect for some elements.

FIG. 8 shows an example mass defect plot with regions of interest

FIG. 9 shows an example Cl—H mass defect plot with Mass Defect (IUPAC)on the y-axis and m/z on the x-axis.

FIG. 10 shows the resulting labeled mass defect plot labeled accordingto the operations and method.

FIG. 11 is schematic view of an example computing device that may beused to implement the systems and methods described in this document.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

Referring to FIG. 1, in a time-of-flight (TOF) mass spectrometer (MS)100, a mass M of an ion 10 can be determined by accelerating ion(s) 10along a flight path (e.g., using an electric field), measuring a flighttime T of the ion(s) 10, and determining the mass M of the ion(s) 10 byusing a relationship of the time-of-flight T as a function of the mass M(e.g., a mass calibration equation). For example, the time-of-flight Tof each ion 10 can be determined using the following equation:

$\begin{matrix}{{T = {\frac{d}{\sqrt{2U}}\sqrt{\frac{M}{z}}}},} & (1)\end{matrix}$where d is a flight path length of the ion 10, M is a mass of the ion10, z is a charge of the ion 10, and U is an electric potentialdifference (voltage) used to accelerate the ion 10. Accelerating ions 10with a known electric field strength U, results in each ion 10 havingthe same kinetic energy as any other ion 10 that has the same charge z.Since a velocity of the ion 10 depends on its mass-to-charge ratio(m/z), the time that it subsequently takes for an ion 10 to travel alongthe flight path and reach a detector 130 (i.e., time-of-flight T) can bemeasured. Heavier ions 10 travel relatively slower and relatively longerflight times T than lighter ions 10. The measurements determined by thedetector 130 are returned as data 140 (see FIG. 2) to the computersystem 1100 for processing (see FIG. 2).

FIG. 1 provides a schematic view of an exemplary time-of-flight massspectrometer (TOF-MS) system 100 that includes an ion source assembly110 (e.g., an accumulating ion source with transfer ion optics and anorthogonal accelerator) in communication with a TOF analyzer 120 (e.g.,a planar multi-reflecting TOF (M-TOF) analyzer) and a detector 130. Theion source assembly 110 accelerates ions 10 (e.g., packets of ions)through the TOF analyzer 120 having a flight path and correspondingflight path length d and into the detector 130.

FIG. 2 provides a schematic view of a TOF-MS 100 and gas chromatographsystem 200. The gas chromatograph 200 utilizes a capillary column,multiple capillary columns or column set with given dimensions and phaseproperties. In some examples, the gas chromatograph 200 may be a liquidchromatograph. A sample is introduced into the column, and depending onthe difference in the chemical properties of the molecules in themixture and the affinity of the molecules to remain stationary withinthe column, the column promotes separation of the molecules. The samplemay be introduced by direct insertion probes or pyrolysis. In someexamples, the sample is introduced without a chromatography. Themolecules elute from the column at different times based on theretention time. The TOF-MS 100 captures, ionizes, accelerates, deflects,focuses and detects the ionized molecules separately as they arereleased from the gas chromatograph 200. The TOF-MS 100 and gaschromatograph system 200 are given for an example and context, it shouldbe understood that any system or mass spectrometer capable ofdetermining the accurate mass of ions may be suitable, including systemsthat permit non-chromatographic or direct sample introduction.

FIG. 3 provides an exemplary arrangement of operations 300 for labelinga mass defect plot (MDP) 900 with halogen filtering. While halogenfiltering is exemplary described herein, it is to be appreciated that itis but one embodiment and other elements or combinations of elements maybe utilized. At block 302, the operations 300 include generating a Cl—Hor Br—H mass defect plot from the summed mass spectra of raw data ordeconvoluted data (sum of individual peak mass spectrum). The data 140may be determined from data 140 provided by the TOM-MS 100 and plottedon a scale of the atomic mass defect (IUPAC) or other suitable scale. Atblock 304, the operations 300 include filtering all ions 10 in the MDP900 that do not have an associated M+2 or M−2 ion (Mass difference of1.997050 Da for chlorine, or 1.997953 Da for bromine) with a scaled massdefect within a specified tolerance. An example tolerance may be 0.0007Da, and relative abundance±15% that does not match a theoretical Br_(x),Cl_(y), or Br_(x)Cl_(y), (where x may be an integer from 1 to 15inclusive and y may be an integer from 1 to 15 inclusive) isotopepattern 150 in at least one embodiment of the invention. Alternatively,tolerances may be calculated as described below in the relation to FIGS.4 and 5.

At block 306, the operations 300 include selecting the most abundantunidentified ion 10, and determining the isotope pattern 150 for theisotopic cluster 152 i.e., Br₂ or BrCl₂, etc. At block 308, theoperations 300 include formula searching for the formula containing theelements identified by the isotope pattern 150, in addition to othercommon elements using, for example, standard combinatorial approaches.Other common elements include, but are not limited to Carbon, Hydrogen,Nitrogen, Oxygen, Sulfur, and/or Phosphorus, etc. At block 308, theoperations 300 include labeling the isotope cluster 152 with thechemical formula determined. In some embodiments, the operations 300include assigning a unique color, symbol or identifier to the isotopecluster 152. At block 310, the operations include labeling the isotopecluster 152 with the chemical formula determined in block 308. At block312, the operations include searching homologous series (±Cl or ±Br),and RDBE related species (±H or ±2H) and labeling with the same color asthe isotope cluster 152 identified and colored in block 310. In someembodiments, at block 314, the operations include showing the selectedion 10 as an extracted ion chromatogram±mass tolerance (i.e., 207.1547±5ppm or ±0.001 Da), and identifying the chromatographic peak(s)corresponding to the extracted ion chromatogram. In some embodiments,the operation blocks 306 through 314 are repeated until all ions in theMDP 900 are identified.

FIG. 4 provides an example method 400 to determine spacing signals andrelative isotopic abundance. At block 402 the spacing tolerance Tm isdetermined. A confidence interval, or m/z tolerance (T_(m)), for thedifference between centroids of two recorded spectral signals of similarm/z is given by Equation 2:T _(m)=(±K _(m))(0.42466)(m)(R _(FWHM))⁻¹[(n ₁)⁻¹+(n ₂)⁻¹]^(1/2)  (2)where K_(m) is the m/z confidence interval width divided by σ, 0.42466is σ divided by Full Width at Half-Maximum height (FWHM), m is the m/zcentroid of spectral signal 1 approximately equal to the m/z centroid ofspectral signal 2, R_(FWHM) is the expected resolving power athalf-maximum height of the signals, n₁ is the number of ions 10 in thespectral signal 1 (the more intense signal) and n₂ is the number of ions10 in spectral signal 2 (the less intense signal).

To simplify implementation and enhance automated deisotoping speed,parameters may be eliminated via simplifying approximations. Eliminatingn₁ or n₂ permits a single T_(m) to be used for each stick, rather than adifferent T_(m) for each pair of sticks compared. By definition, n₁ willnot be less than n₂, thus n₁ will not contribute more to T_(m) than n₂,and eliminating n₁ is more prudent than eliminating n₂. Assuming n₁ isapproximately equal to n₂ and substituting n₂ for n₁ in Equation 2yields Equation 3:T _(m)=(±K _(m))(0.42466)(m)(R _(FWHM))⁻¹(2)^(1/2)(n ₂)^(1/2)  (3)

The number of ions, (n₂), is related to the spectral area of signal 2 asin Equation 4:n ₂=(a ₂)(i ₂)  (4)where, a₂ is the spectral area of signal 2, and i₂ is the ions 10 perarea expected at the mass of signal 2.

When tuning the detector, ions 10 per area is estimated for the tunemass. It is expected that some detectors may register weaker signals forheavier, hence slower ions 10 and stronger signals for lighter, hencefaster ions 10. For such detectors, if detector response is directlyproportional to ion 10 velocity and ions per area is estimated for asingle tune mass, then the ions 10 per area expected at the mass ofsignal 2, (i₂), is related to the ions 10 per area at the tune mass byEquation 5:i ₂=(i _(tune))(m/m _(tune))^(1/2)  (5)where, i_(tune) is the ions 10 per area for the tune mass by detectortune or measurement, and m_(tune) is the m/z of the tune mass used bydetector tune or detector measurement.

Substituting Equation 5 into Equation 4, substituting the result intoEquation 3, and partially simplifying yields Equation 6:T _(m)=(±K _(m))(2)^(1/2)(0.42466)(m)(R _(FWHM))⁻¹[(a ₂)(i _(tune))(m/m_(tune))^(1/2)]^(−1/2)  (6)

Further simplification would show that the m/z tolerance, (T_(m)), isexpected to vary with the ¾ power of the observed m/z (m). Thus, formasses heavier than the tune mass, the T_(m) predicted by Equation 6will be narrower than the T_(m) predicted by assuming detector responseis independent of ion mass. Likewise, for masses lighter than the tunemass, the T_(m) predicted by Equation 6 will be wider than the T_(m)predicted by assuming detector response is independent of ion mass. Notethat not all detectors register weaker signals for heavier, hence slowerions 10 and stronger signals for lighter, hence faster ions 10, thus forsome detectors, the m/z tolerance will vary linearly with observed m/z,or the m/z tolerance will as a different function of m/z.

A practical value for K_(m) is about 2.8, corresponding to about 99.5%confidence. Multiplying 2.8 by the square root of 2 gives a convenientvalue of about 4. The recommended spacing tolerance for detectors whereresponse is directly proportional to ion velocity is then given byEquation 7:T _(m)=(±4)(0.42466)(m)(R _(FWHM))⁻¹[(a ₂)(i _(tune))(m/m_(tune))^(1/2)]^(−1/2)  (7)

To permit facile substitution of resolution at other peak heights intoEquation 7, the constant converting R_(FWHM) to σ (0.42466) is notcombined with the confidence interval factor (±4).

Empirical verification of spacing tolerances predicted by Equation 7 isillustrated in FIG. 5. Replicate injections of tetrabromobenzothiophenewere recorded, spacing of all bromine isotopologues of the molecular ionin all spectra of all injections was calculated, and estimated number ofions 10 in the minor isotopologue was plotted against spacing. Spacingtolerances predicted by Equation 7 are shown by the curve 502. Forcomparison, spacing tolerances predicted using the linear-with-m model(i.e. the model assuming detector response is independent of m/z), areshown by the curve 504. A total of 1230 individual bromine isotopologuepairs 506 are shown in the plot. FIG. 5 shows spacing of Brisotopologues for the molecular ion of C₄Br₄S (nominal m/z 400). Theions 10 per area were measured at nominal m/z 219. Expected R_(FWHM) atm/z 400 is 35,000.

Additional adjustments to spacing tolerances may be implemented to avoidstatistically-based tolerances that are too narrow at very large n orsmall m/z, and to avoid tolerances that are too wide at very low n andlarge m/z. To avoid the latter case, tolerance width may be limited tothe width corresponding to a number of ions 10 that can be quantitatedwith reasonable accuracy. Expected CV for the area of 25 ions 10 isabout 20%; the corresponding upper limit for tolerance width is capturedin Equation 8:T _(m)=(±4)(0.42466)(m)(R _(FWHM))⁻¹{MAX[(25),(a ₂)(i _(tune))(m/m_(tune))^(1/2)]}^(−1/2)  (8)

Capping the tolerance width at the width expected for 25 ions 10 wouldreject 2 of 1230 isotope pairs plotted in FIG. 5. In both pairs thatwould be rejected, the minor isotope has a total of less than 16 ions.

At very large nor small m/z, non-statistical contributions to isotopesignal spacing may dominate statistical contributions. A finaladjustment may be to override the T_(m) predicted by Equation 8 with auser-specified minimum tolerance, as in Equation 9:T=MAX[T _(user) ,T _(m)]  (9)

A reasonable default for T_(user)=1.5 mDa.

At block 404, the method 400 includes determining the relative abundancetolerances for M+1 signals. After putative pairs of isotopologues arefound within the preceding spacing tolerances, reasonable relativeabundance tolerances may be established by considering the elementcounts predicted by the pairs of putative isotopologues.

For GC-amenable analytes typically encountered in petroleum, biological,food, or environmental samples, the principal contributions to total M+1relative abundance are ¹³C, ¹⁵N, ²⁹Si, and ³³S, with minor contributionsfrom ¹⁷O and ²H. As boron-containing and metal-containing analytes arerarely encountered in the preceding sample types, such analytes are notconsidered in the following discussion.

Of likely M+1 contributors, ²⁹Si is expected to contribute the greatestrelative abundance per unit of mass. Thus, for any putative isotopeduster in a spectrum, the most tolerant assumed possible elementalcomposition is pure terrestrial silicon. Silicon count is then predictedfrom relative abundance of the putative M+1 signal. For a true M+1signal, the predicted silicon count including an appropriate tolerancecannot exceed the measured monoisotopic mass divided by 28.

Prediction of silicon count from M+1 relative abundance is given inEquation 10, and generalized to any “M+1” element in Equation 11:Si=[M+1][M]⁻¹[0.0508]⁻¹  (10)where Si is the predicted maximum silicon count in the formula, [M+1] isthe intensity of putative M+1 signal, [M] is the intensity of putativemonoisotopic signal, and 0.0508 is the terrestrial natural abundance of²⁹Si.C _(M+1)=[M+1][M]⁻¹[A]⁻¹  (11)where, C_(M+1) is the predicted max. count of an “M+1” element(principally C, N, Si), [M+1] is the intensity of putative M+1 signal,[M] is the intensity of putative monoisotopic signal, and A is theterrestrial natural abundance of an element.

Ion statistics fundamentally limit the certainty of predicted elementcounts given by Equation 11. A confidence interval, or element counttolerance (T_(c)), about the predicted element count (C) is given byEquation 12:T _(c)=(±K _(c))(C)(n _(p))^(−1/2)[2+(AC)+(AC)⁻¹]^(1/2)  (12)where, C is the predicted count of an element, K_(c) is the elementcount confidence interval/σ; a reasonable value is 2.8, corresponding toabout 99.5% confidence, and n_(p) is the total number of ions 10 in thepair of putative isotopologue signals; note that equivalent renditionsof Equation 12, using n_(M) or n_(M+1) in place of n_(p) could bederived by using the relationships n_(M)+n_(M+1)=n_(p) andn_(M+1)/n_(M)=AC.

Thus, a provisionally assigned M+1 signal found by m/z spacing may berejected as false M+1 assignment if the predicted maximum silicon count(from Equation 10) minus the element count tolerance (T_(c), fromEquation 12) is greater than the measured monoisotopic mass divided by28. If silicon-containing compounds are not analytes of interest in aparticular analysis, the M+1 relative abundance threshold may be basedon pure terrestrial carbon. In this case, predicted carbon count wouldbe given by Equation 11, where A=0.0108. This predicted carbon countminus the carbon count tolerance from Equation 12 should not exceed themeasured monoisotopic mass divided by 12.

At block 406, the method 400 includes determining relative abundancetolerances for M+2 signals. Testing putative ³⁴S signals should besimilar to testing putative M+1 signals; the predicted sulfur countminus the sulfur count tolerance from Equation 12 should not exceed themeasured monoisotopic mass divided by 32.

Chlorinated and brominated analytes exhibit strong characteristicisotope patterns with multiple detectable isotopologues in a series (M,M+2, M+4, M+6, . . . ). Within a valid series of ³⁷Cl, ⁸¹Br, or mixedhalogen isotopologues, there will always be at least one adjacenthalogen isotopologue pair of relative abundance difference not less thanthe terrestrial natural abundance of ³⁷Cl, subject to statisticallyvalid relative abundance tolerances. Thus, a series of putative halogenisotopologues should be rejected if all pairs of adjacent members yielda predicted chlorine count less than one minus the tolerance given byEquation 12. Alternatively, more thorough approaches to testing putativehalogen patterns are possible, but may be computationally cumbersome.Some of the details are discussed below.

At block 408, the method 400 includes determining the alternativerelative abundance tolerances for chlorinated or brominated isotopepatterns. Chlorinated and brominated analytes can exhibit strong isotopeclusters with multiple even (M, M+2, M+4, etc.) isotopologues ofsignificant abundance (>10% relative to the most abundant isotopologue).Putative members of such strong isotope clusters may be confirmed orrejected by requiring predicted element counts to agree for adjacentpairs of putative isotopologues. For a typical organic compound thatcontains chlorine or bromine but not both elements, Equation 11 may beextended to higher isotopologue pairs as in Equations 13 to 16, and canbe further generalized if desired.C _(M+2)=[M+2][M]⁻¹[A]⁻¹  (13)C _(M+2)=1+(2)[M+4][M+2]⁻¹[A]⁻¹  (14)C _(M+2)=2+(3)[M+6][M+4]⁻¹[A]⁻¹  (15)C _(M+2)=3+(4)[M+8][M+6]⁻¹[A]⁻¹  (16)where, C_(M+2) is the predicted maximum count of an “M+2” element(principally Cl, Br), [M] is the intensity of putative monoisotopicsignal, [M+2] is the intensity of putative M+2 signal, [M+4] is theintensity of putative M+4 signal, [M+6] is the intensity of putative M+6signal, [M+8] is the intensity of putative M+6 signal, and A is theterrestrial natural isotopic abundance (principally ³⁷Cl, ⁸¹Br).

The tolerance from Equation 12 can be validly applied to Equation 13 fora putative M+2/M pair, but may under-estimate the uncertainty inrelative abundance for higher isotopologue pairs. Valid generalizationof Equation 12 to higher isotopologue pairs may be computationallycumbersome. A more practical approach is to predict chlorine or brominecount (C_(M+2)) for each pair of adjacent putative M+2 isotopologues andaccept a putative isotope cluster if predicted chlorine or bromine countis consistent for all adjacent isotopologue pairs. Loose tolerancesshould be applied; requiring predicted element counts to agree to withina factor of 2 is reasonable. The above may be used to determine the MDP900 that do not have an associated M+2 or M−2 ion.

At block 410, the method includes determining minimum monoisotopic massfor a brominated analyte using virtual monoisotopic bromine. Highlybrominated analytes can exhibit monoisotopic signals markedly weakerthan the most abundant isotopologue. For highly brominated analytes, thelikelihood of a quantifiable most abundant isotopologue belonging to anundetectable monoisotopic signal warrants permitting virtual bromineisotopologues to be considered. A reasonable maximum number of virtualbromine isotopologues to add is twice the sum of the number of detectedbromine isotopologues minus two. Thus, if three bromine isotopologuesare detectable, the detected isotopologue of lowest mass may be testedas M (no virtual bromine isotopologues) or M+2 (two virtual bromineisotopologues; one on each side of the detected isotope cluster). Iffour bromine isotopologues are detectable, the detected isotopologue oflowest mass may be tested as M (no virtual bromine isotopologues), M+2(two virtual bromine isotopologues; one on each side of the detectedisotope cluster), or M+4 (four virtual bromine isotopologues; two oneach side of the detected isotope cluster).

The monoisotopic mass must be sufficient to support the number ofbromines predicted by the isotope cluster, plus the number of carbonsrequired to support additional bromines beyond two. Minimum monoisotopicmass for a brominated analyte is given by Equation 17, Equation 18, andEquation 19.Mass_(min)=(79)(C _(Br))+(12)(C _(Cmin))  (17)

where, Mass_(min) is the minimum monoisotopic mass for a brominatedanalyte, C_(Br) is the Br count predicted by the number of Brisotopologues (Eq. 18), and C_(Cmin) is the Minimum C count required tosupport the Br count (Eq. 19).C _(Br)=(sum of detected and virtual bromine isotopologues)−1  (18)C _(Cmin)=(C _(Br)−2)(2)⁻¹  (19)

Any fractional value of C_(Cmin) in Equation 19 is always rounded up tothe greater integer. Virtual bromine isotopologues cannot be added ifthe resulting monoisotopic mass would be less than Mass_(min) fromEquation 17.

At block 412, the method 400 includes determining the relative M+2intensity relative to the M intensity for mixed halogen patterns.Analytes containing both Cl and Br will exhibit isotope patterns that donot yield consistent predicted element counts using the form ofEquations 12 to 16. For such mixed halogens, total M+2 intensityrelative to M intensity is given by Equation 20:[M+2][M]⁻¹ =A _(Cl) C _(Cl) +A _(Br) C _(Br)  (20)where, A_(Cl) is the terrestrial natural abundance of ³⁷Cl, C_(Cl) isthe chlorine count in the formula, A_(Br) is the terrestrial naturalabundance of ⁸¹Br, and C_(Br) is the bromine count in the formula. Thetotal M+4 intensity relative to M intensity is given by Equation 21:[M+4][M]⁻¹=½A _(Cl) ²(C _(Cl) ² −C _(Cl))+A _(Cl) C _(Cl) A _(Br) C_(Br)+½A _(Br) ²(C _(Br) ² −C _(Br))  (21)

If either C_(Cl) or C_(Br) is zero, Equation 21 can be divided byEquation 20 and the result rearranged to yield Equation 13. From [M],[M+2], and [M+4], the system of Equations 20 and 21 should yield a realand plausible solution for C_(Cl) and C_(Br).

FIG. 6 shows an example extracted ion chromatogram and total ionextracted ion chromatogram 600 based on the data 140 from the TOF-MS100. The extracted ion chromatogram total ion and extracted ionchromatogram 600 includes an x-axis for time and a y-axis for signalintensity. Individual peaks 610 are shown in the extracted ionchromatogram and total ion extracted ion chromatogram 600 related to theindividual detection of ions 10 by the detector 130. The data 140 may beused to determine the mass defect of these ions. Further, the extractedion chromatogram 600 may allow a user to select a region of interest andlimit mass filtering and mass defect analysis to obtain more accurateresults.

FIG. 7 shows an example graph for mass defect. The mass defect may bedetermined by equation 22.Mass Defect=Exact Mass−Nominal Mass.  (22)

For example, the mass defect is centered around carbon having an atomicweight of 12.0000 in accordance with IUPAC. Considering C₃H₈, C₃H₈ hasan exact mass of 44.06205 and a nominal mass of 44.00000, the resultingmass defect is 0.06205. By comparison, C₃Cl₆ has an exact mass of281.81257 and a nominal mass of 282.00000, resulting in a mass defect of−0.18743. The graph here shows atomic mass defects for some commonisotopes. For example, ¹H has a mass defect of less than 0.01 and ²H hasa mass defect of approximately 0.015, allowing them to be easilydistinguished. Even isotopes with similar atomic mass may bedifferentiated using mass defect and have substantially different massdefect values. For example, ¹⁵N and ¹⁶O, which have an atomic mass of15.0001 and 15.99491 respectively have a significant difference in massdefect of approximately 0.0001 and 0.005 respectively.

FIG. 8 shows an example mass defect plot 800 with regions 810 ofinterest. When the mass defect is calculated from the data 140 andplotted based on the y-axis being mass defect with Carbon 12 as zeromass defect and the m/z on the x-axis, multiple regions of interestappear. Alkanes will generally appear in the alkanes region 810 a,siloxanes will generally appear in the siloxanes region 810 b, andhalogenated compounds will generally appear in the halogenated compoundregion 810 c. This is useful allowing the user to identify a specificcompound of interest limited to the amount of data 140 that must beprocessed to determine the specific ions 10 and/or compound formula.

FIG. 9 shows an example Cl—H mass defect plot 900 with Mass Defect(IUPAC) on the y-axis and m/z on the x-axis. Alternative computationsmay be applied to generate a Kendrick mass defect plot where CH₂ isconsidered to be exactly 14 Da instead of the IUPAC mass for CH₂ whichis considered to be 14.01565. The Kendrick mass is defined in equation23.Kendrick Mass=IUPAC Mass*(14.00000/14.01565)  (23)

The scaled mass defect may be determined by first solving the scaledmass of equation 24.Scaled mass=IUPAC mass*Scaling Factor  (24)

The particular scaling factor for the graphs presented of Cl—H is34/33.96048. The scaled mass defect may be determined by equation 25.Scaled Mass Defect=Scaled Mass−Nominal Scaled Mass  (25)

Each point 910 on the Cl—H corresponds to a peak on the extracted ionchromatogram as seen in FIG. 6. The individual points 910 representcompounds and/or ions 10 to be identified by the computer system usingoperations 300 and method 400 in accordance with FIGS. 3 and 4.

FIG. 10 shows the resulting labeled mass defect plot 1100 that may belabeled according to the operations 300 and method 400 in FIGS. 3 and 4.After the formula search has been performed in block 308 and the ion 10elements identified, the mass defect plot is labeled showing individualcompounds. Each dot 910 may be given a symbol or a color to identify thecompound it corresponds to. Each dot 910 or compound may also include alabel 1010 to make identification easier. The label 1010 may correspondto multiple dots 910. A key or index 1020 may be displayed by thecomputer 1100 or display to properly determine the related compound.

In at least one example, a user uses a TOF-MS 100 or other suitable massspectrometry system to analyze a sample. The ions 10 from the sample mayimpact the detector 130 resulting in data 140 being delivered to acomputing device 100 attached to the TOF-MS 100. The time and energy ofthe ions 10 impacting the detector 130 may be graphed as an ionchromatogram 600 based on the data 140 with the x-axis being the timeand the y-axis being the signal intensity. The ion chromatogram 600 maybe presented to the user via a display 1180 allowing the user to obtaina user selection related to a selection of data that the user isinterested in. The user selection may be a click, touch gesture, caliperselection or any suitable form to select the raw or processed data theuser may be interested in. A user may select a mass defect plotgeneration and input additional attributes including the data source,the mode, filters, reference formula, defect polarity, defectadjustment, and/or auto updating. In at least one example, the datasource is caliper, the mode is scaled mass defect, the abundance filteris a minimum and has a value of 0.1, the reference formula is CH₂, thedefect polarity is positive, the defect adjustment is 0, and auto updateis enabled. The computer system 1100 may generate a mass defect plot800, 900 based on the data source. The mass defect may be determinedusing equation 22 above. In at least one embodiment, the mass defectplot 800, 900 may be filtered using a specified Da value and relativeabundance. In at least one embodiment, statically sound spacing andrelative abundance tolerances are determined. For example, a K_(m) valueof approximately 2.8 may be used, and using equation 7 a T_(m)(confidence interval, or m/z tolerance) may be determined based on them/z centroid of spectral signal 1 and/or 2, expected resolving power athalf-maximum height of the signals, spectra area of signal 2, ions perarea for the tune mass by detector tune or measurement, and/or the m/zof the tune mass used by detector tune or detector measurement. TheT_(m) may be expanded or reduced to avoid statistically based tolerancesthat are too narrow at very large n or small m/z value and to avoidtolerances that are too wide at very low n and large m/z values. In someexamples, the T_(m) value is limited by a user input. Next the relativeabundances of M+1 may be determined generally using equation 10, and theconfidence interval may be determined using equation 12. The primarycontributors to the M+1 counts are ¹³C, ¹⁵N, ²⁹Si, and ³³S, with minorcontributions from ¹⁷O and ²H. In some examples, the silicon equation 10may be used. Equations 10 and 11 may be determined based on theintensity of the putative M+1 signal, the intensity of the monoisotopicsignal, and the terrestrial natural abundance of the element inquestion. Equation 12 also includes the predicted count of an element,the element count confidence interval divided by σ, and the total numberof ions in the pair of putative isotopologue signals. Next the M+2signals may be determined. In some examples, the relative abundancetolerances are determined separately in chlorinated or brominatedisotope patterns using equations 13-16 based on the predicted maximumcount of an “M+2” element (principally Cl, Br), the intensity ofputative monoisotopic signal, the intensity of putative M+2 signal, theintensity of putative M+4 signal, the intensity of putative M+6 signal,the intensity of putative M+8 signal, and A is the terrestrial naturalisotopic abundance (principally ³⁷Cl, ⁸¹ Br). In other examples wherethere is a high amount of brominated analytes, equations 17-19 may beused to determine the minimum monoisotopic mass of the brominatedanalyte based on the Br count predicted by the number of Brisotopologues and the Minimum Ccount required to support the Br count.In examples where there is mixed halogen patterns, such as Cl and Br,equations 20 and 21 may be used to determine the M+2 and M+4 intensitybased on the terrestrial natural abundance of ³⁷Cl, the chlorine countin the formula, the terrestrial natural abundance of ⁸¹Br, and thebromine count in the formula. The resulting values may be used to filterthe data 140 into isotope patterns 150 and isotopic clusters 152. Thecomputing device 1100 may select the most abundant unidentified ion 10in the selected data and determine the isotopic pattern 150 for theisotopic cluster 152. Formula searching may be performed to determineelements identified by the isotopic pattern 150. For example, with anisotopic pattern for chlorine, formulas containing chlorine would besearched. In some examples, common elements, such as Carbon, Hydrogen,Nitrogen, Oxygen, Sulfur, and/or Phosphorus are also searched todetermine if the formula contains these elements to determine if theunidentified ion matches the formula mass defect. After determining theformula for the unidentified ion, the computer system 1100 may label theunidentified ion 10 on a labeled mass defect plot 1000, and the computersystem may color and/or mark the displayed ion 10 on the labeled massdefect plot 1000. After identifying the ion 10, the computer system 1000searches homologous series (±Cl or ±Br), and RDBE related species (±H or±2H) and labeling with the same color or identifier as the isotopecluster 152. In some examples, the user may select an ion 10 or thecomputer system may select an ion 10 and display to the user anextracted ion chromatogram±mass tolerance with the peaks 610 for the ionidentified.

FIG. 11 is schematic view of an example computing device 1100 that maybe used to implement the systems and methods described in this document.The computing device 1100 is intended to represent various forms ofdigital computers, such as laptops, desktops, workstations, personaldigital assistants, servers, blade servers, mainframes, and otherappropriate computers. The components shown here, their connections andrelationships, and their functions, are meant to be exemplary only, andare not meant to limit implementations of the inventions describedand/or claimed in this document.

The computing device 1100 includes a processor 1110, memory 1120, astorage device 1130, a high-speed interface/controller 1140 connectingto the memory 1120 and high-speed expansion ports 1150, and a low speedinterface/controller 1160 connecting to low speed bus 1170 and storagedevice 1130. Each of the components 1110, 1120, 1130, 1140, 1150, and1160, are interconnected using various busses, and may be mounted on acommon motherboard or in other manners as appropriate. The processor1110 can process instructions for execution within the computing device1100, including instructions stored in the memory 1120 or on the storagedevice 1130 to display graphical information for a graphical userinterface (GUI) on an external input/output device, such as display 1180coupled to high speed interface 1140. In other implementations, multipleprocessors and/or multiple buses may be used, as appropriate, along withmultiple memories and types of memory. Also, multiple computing devices1100 may be connected, with each device providing portions of thenecessary operations (e.g., as a server bank, a group of blade servers,or a multi-processor system).

The memory 1120 stores information non-transitorily within the computingdevice 1100. The memory 1120 may be a computer-readable medium, avolatile memory unit(s), or non-volatile memory unit(s). Thenon-transitory memory 1120 may be physical devices used to storeprograms (e.g., sequences of instructions) or data (e.g., program stateinformation) on a temporary or permanent basis for use by the computingdevice 1100. Examples of non-volatile memory include, but are notlimited to, flash memory and read-only memory (ROM)/programmableread-only memory (PROM)/erasable programmable read-only memory(EPROM)/electronically erasable programmable read-only memory (EEPROM)(e.g., typically used for firmware, such as boot programs). Examples ofvolatile memory include, but are not limited to, random access memory(RAM), dynamic random access memory (DRAM), static random access memory(SRAM), phase change memory (PCM) as well as disks or tapes.

The storage device 1130 is capable of providing mass storage for thecomputing device 1100. In some implementations, the storage device 1130is a computer-readable medium. In various different implementations, thestorage device 1130 may be a floppy disk device, a hard disk device, anoptical disk device, or a tape device, a flash memory or other similarsolid state memory device, or an array of devices, including devices ina storage area network or other configurations. In additionalimplementations, a computer program product is tangibly embodied in aninformation carrier. The computer program product contains instructionsthat, when executed, perform one or more methods, such as thosedescribed above. The information carrier is a computer- ormachine-readable medium, such as the memory 1120, the storage device1130, or memory on processor 1110.

The high speed controller 1140 manages bandwidth-intensive operationsfor the computing device 1100, while the low speed controller 1160manages lower bandwidth-intensive operations. Such allocation of dutiesis exemplary only. In some implementations, the high-speed controller1140 is coupled to the memory 1120, the display 1180 (e.g., through agraphics processor or accelerator), and to the high-speed expansionports 1150, which may accept various expansion cards (not shown). Insome implementations, the low-speed controller 1160 is coupled to thestorage device 1130 and low-speed expansion port 1170. The low-speedexpansion port 1170, which may include various communication ports(e.g., USB, Bluetooth, Ethernet, wireless Ethernet), may be coupled toone or more input/output devices, such as a keyboard, a pointing device,a scanner, or a networking device such as a switch or router, e.g.,through a network adapter.

The computing device 1100 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 1100 a or multiple times in a group of such servers 1100a, as a laptop computer 1100 b, or as part of a rack server system 1100c.

Various implementations of the systems and techniques described here canbe realized in digital electronic and/or optical circuitry, integratedcircuitry, specially designed ASICs (application specific integratedcircuits), computer hardware, firmware, software, and/or combinationsthereof. These various implementations can include implementation in oneor more computer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium” and“computer-readable medium” refer to any computer program product,non-transitory computer readable medium, apparatus and/or device (e.g.,magnetic discs, optical disks, memory, Programmable Logic Devices(PLDs)) used to provide machine instructions and/or data to aprogrammable processor, including a machine-readable medium thatreceives machine instructions as a machine-readable signal. The term“machine-readable signal” refers to any signal used to provide machineinstructions and/or data to a programmable processor.

Implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them. Moreover,subject matter described in this specification can be implemented as oneor more computer program products, i.e., one or more modules of computerprogram instructions encoded on a computer readable medium for executionby, or to control the operation of, data processing apparatus. Thecomputer readable medium can be a machine-readable storage device, amachine-readable storage substrate, a memory device, a composition ofmatter effecting a machine-readable propagated signal, or a combinationof one or more of them. The terms “data processing apparatus”,“computing device” and “computing processor” encompass all apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.The apparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them. A propagated signal is an artificially generated signal, e.g.,a machine-generated electrical, optical, or electromagnetic signal, thatis generated to encode information for transmission to suitable receiverapparatus.

A computer program (also known as an application, program, software,software application, script, or code) can be written in any form ofprogramming language, including compiled or interpreted languages, andit can be deployed in any form, including as a stand-alone program or asa module, component, subroutine, or other unit suitable for use in acomputing environment. A computer program does not necessarilycorrespond to a file in a file system. A program can be stored in aportion of a file that holds other programs or data (e.g., one or morescripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub programs, or portionsof code). A computer program can be deployed to be executed on onecomputer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, e.g., a mobile telephone, a personal digital assistant(PDA), a mobile audio player, a Global Positioning System (GPS)receiver, to name just a few. Computer readable media suitable forstoring computer program instructions and data include all forms ofnon-volatile memory, media and memory devices, including by way ofexample semiconductor memory devices, e.g., EPROM, EEPROM, and flashmemory devices; magnetic disks, internal hard disks or removable disks;magneto optical disks; and CD ROM and DVD-ROM disks. The processor andthe memory can be supplemented by, or incorporated in, special purposelogic circuitry.

To provide for interaction with a user, one or more aspects of thedisclosure can be implemented on a computer having a display device,e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, ortouch screen for displaying information to the user and optionally akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

One or more aspects of the disclosure can be implemented in a computingsystem that includes a backend component, e.g., as a data server, orthat includes a middleware component, e.g., an application server, orthat includes a frontend component, e.g., a client computer having agraphical user interface or a Web browser through which a user caninteract with an implementation of the subject matter described in thisspecification, or any combination of one or more such backend,middleware, or frontend components. The components of the system can beinterconnected by any form or medium of digital data communication,e.g., a communication network. Examples of communication networksinclude a local area network (“LAN”) and a wide area network (“WAN”), aninter-network (e.g., the Internet), and peer-to-peer networks (e.g., adhoc peer-to-peer networks).

The computing system can include clients and servers. A client andserver are generally, remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someimplementations, a server transmits data (e.g., an HTML page) to aclient device (e.g., for purposes of displaying data to and receivinguser input from a user interacting with the client device). Datagenerated at the client device (e.g., a result of the user interaction)can be received from the client device at the server.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the disclosure or of what maybe claimed, but rather as descriptions of features specific toparticular implementations of the disclosure. Certain features that aredescribed in this specification in the context of separateimplementations can also be implemented in combination in a singleimplementation. Conversely, various features that are described in thecontext of a single implementation can also be implemented in multipleimplementations separately or in any suitable sub-combination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multi-tasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made without departingfrom the spirit and scope of the disclosure. Accordingly, otherimplementations are within the scope of the following claims. Forexample, the actions recited in the claims can be performed in adifferent order and still achieve desirable results.

What is claimed is:
 1. A method of constructing a filtered mass defectplot based on mass data acquired from a mass spectrometer, the methodcomprising: generating a mass defect plot from data obtained from themass spectrometer; selecting an unidentified ion, determining an isotopepattern for the unidentified ion; identifying one or more elementsindicated by an isotope pattern for the unidentified ion; searchingformulas containing one or more elements indicated by the isotopepattern for the unidentified ion; determining a chemical formula of theunidentified ion; displaying the chemical formula for the unidentifiedion on a screen; receiving a user selection of an ion; displaying theselected ion as an extracted ion chromatogram ±mass tolerance; andidentifying one or more chromatographic peak corresponding to theextracted ion chromatogram ±mass tolerance.
 2. The method of claim 1further comprising identifying homologous series and deuterium and/orhydrogen related to the unidentified ion.
 3. The method of claim 2,wherein the formulas in the homologous series contain elements withcharacteristic isotopologues that include chlorine and bromine.
 4. Themethod of claim 1, the data is raw data from the mass spectrometer. 5.The method of claim 1, the data is deconvoluted data from the massspectrometer.
 6. The method of claim 1, further comprising labeling thechemical formulas for the unidentified ion on the screen.
 7. The methodof claim 1, further comprising assigning a color or identifier to theunidentified ion on the screen.
 8. The method of claim 1, the massdefect plot is an iterative addition of a chemical formula.
 9. Themethod of claim 8, wherein the mass defect plot is a CH₂ mass defectplot.
 10. The method of claim 1, further comprising filtering all ionsin the mass defect plot that do not have an associated isotopologue ionby filtering the mass defect with a specific relative abundancetolerance.
 11. The method of claim 1, further comprising filtering allions in the mass defect plot that do not have an associated isotopologueion by comprises filtering all ions that do not match Br_(x) isotopepattern, wherein x is an integer between 1 up to and including
 15. 12.The method of claim 1, further comprising filtering all ions in the massdefect plot that do not have an associated isotopologue ion by filteringall ions that do not match Cl_(y) isotope pattern, wherein y is aninteger between 1 up to and including
 15. 13. The method of claim 1,further comprising filtering all ions in the mass defect plot that donot have an associated isotopologue ion by filtering all ions that donot match Br_(x)Cl_(y) isotope pattern, wherein x is an integer between1 up to and including 15 and y is an integer between 1 up to andincluding
 15. 14. The method of claim 1, further comprising filteringall ions in the mass defect plot that do not have an associatedisotopologue ion by filtering by determining a spacing tolerance. 15.The method of claim 14, wherein the spacing tolerance is based on astatic m/z distance between a first signal and a second signal.
 16. Themethod of claim 15, wherein a m/z space tolerance is based on astatistical confidence interval.
 17. The method of claim 15, wherein thespacing tolerance is further based on a number of ions in the firstsignal, and a number of ions in the second signal.
 18. The method ofclaim 14, wherein the spacing tolerance is limited by an user input. 19.The method of claim 14, wherein filtering all ions in the mass defectplot that do not have an associated isotopologue ion filtering bydetermining a relative abundance.
 20. The method of claim 19, whereinthe relative abundance is determined for a M+1 signals.
 21. The methodof claim 20, wherein determining the relative abundance for a M+1signals further comprises determining a maximum predicted count of anM+1 element based on an intensity of a putative M+1 signal, an intensityof a putative monoisotopic signal, and a terrestrial natural abundanceof the M+1 element.
 22. The method of claim 21, wherein the M+1 elementis carbon, nitrogen, or silicon.
 23. The method of claim 19, wherein therelative abundance is determined for a M+2 signals.
 24. The method ofclaim 23, wherein determining the relative abundance for M+2 signalsfurther comprises determining a maximum predicted count of an M+2element based on an intensity of a putative monoisotopic signal, anintensity of a putative M+2 signal and a terrestrial natural abundanceof the M+2 element.
 25. The method of claim 23, wherein determining therelative abundance for M+2 signals further comprises determining amaximum predicted count of an M+2 element based on an intensity of aputative monoisotopic signal, an intensity of a putative M+2 signal, anintensity of a putative M+4 signal and a terrestrial natural abundanceof the M+2 element.
 26. The method of claim 23, wherein determining therelative abundance for M+2 signals further comprises determining amaximum predicted count of an M+2 element based on an intensity of aputative monoisotopic signal, an intensity of a putative M+4 signal, anintensity of a putative M+6 signal and a terrestrial natural abundanceof the M+2 element.
 27. The method of claim 23, wherein determining therelative abundance for M+2 signals further comprises determining amaximum predicted count of an M+2 element based on an intensity of aputative monoisotopic signal, an intensity of a putative M+6 signal, anintensity of a putative M+8 signal and a terrestrial natural abundanceof the M+2 element.
 28. The method of claim 23, wherein determining therelative abundance for M+2 signals further comprises: determining if oneor more analytes contain both chlorine and bromine; and if the one ormore analytes contain both chlorine and bromine determining a maximumpredicted count of an M+2 element based on a terrestrial naturalabundance of ³⁷Chlorine, a terrestrial natural abundance of ⁸¹Bromine.29. The method of claim 1, the filtered mass defect plot is a halogenfiltered mass defect plot.
 30. The method of claim 1, furthercomprising: filtering all ions in the mass defect plot that do not havean associated confirmatory isotopologue.
 31. A device comprising: adisplay; data processing hardware in communication with the display; andmemory hardware in communication with the data processing hardware, thememory hardware storing instructions that when executed on the dataprocessing hardware cause the data processing hardware to performoperations comprising: generating a mass defect plot from data obtainedfrom a mass spectrometer; filtering all ions in the mass defect plotthat do not have an associated isotopologue; selecting an unidentifiedion; determining an isotope pattern for the unidentified ion;identifying one or more elements indicated by the isotope pattern forthe unidentified ion; searching formulas containing one or more elementsindicated by the isotope pattern for the unidentified ion; determining achemical formula related to the unidentified ion; displaying thechemical formulas for the unidentified ion on a display; receiving auser selection of an ion; displaying the selected ion as an extractedion chromatogram ±mass tolerance; and identifying one or morechromatographic peak(s) corresponding to the extracted ion chromatogram±mass tolerance.
 32. The device of claim 31, wherein the operationsfurther comprise, identifying homologous series and deuterium and/orhydrogen related to the unidentified ion.
 33. The device of claim 32,wherein the formulas in the homologous series contain elements withcharacteristic isotopologues that comprise chlorine and or bromine. 34.The device of claim 31, wherein the data is raw data from a massspectrometer.
 35. The device of claim 31, wherein the data isdeconvoluted data from a mass spectrometer.
 36. The device of claim 31,wherein the operations further comprise, labeling the chemical formulasfor the unidentified ion on the display.
 37. The device of claim 31,wherein the operations further comprise assigning a color to theunidentified ion on the display.
 38. The device of claim 31, wherein themass defect plot is an iterative addition of a chemical formula such asa CH₂ mass defect plot or the like.
 39. The device of claim 31, whereinthe operation filtering all ions in the mass defect plot that do nothave an associated isotopologue ion, filtering the mass defect with aspecific relative abundance tolerance.
 40. The device of claim 31,wherein the operation filtering all ions in the mass defect plot that donot have an associated isotopologue ion, filtering all ions that do notmatch Br_(x) isotope pattern, wherein x is an integer between 1 up toand including
 15. 41. The device of claim 31, wherein the operationfiltering all ions in the mass defect plot that do not have anassociated isotopologue ion, filtering all ions that do not match Cl_(y)isotope pattern, wherein y is an integer between 1 up to and including15.
 42. The device of claim 31, wherein the operation filtering all ionsin the mass defect plot that do not have an associated isotopologue ion,filtering all ions that do not match Br_(x)Cl_(y) isotope pattern,wherein x is an integer between 1 up to and including 15 and wherein yis an integer between 1 up to and including
 15. 43. The device of claim31, wherein the operation filtering all ions in the mass defect plotthat do not have an associated isotopologue ion, filtering bydetermining a spacing tolerance.
 44. The device of claim 43, wherein thespacing tolerance is based on a statistical m/z confidence interval, afirst signal, and a second signal.
 45. The device of claim 44, whereinthe m/z spacing tolerance is based on a statistical confidence interval.46. The device of claim 44, wherein the spacing tolerance is furtherbased on a number of ions in the first signal, and a number of ions inthe second signal.
 47. The device of claim 43, wherein the spacingtolerance is limited by an user input.
 48. The device of claim 43,wherein the operation filtering all ions in the mass defect plot that donot have an associated isotopologue ion, filtering by determining arelative abundance.
 49. The device of claim 48, wherein the relativeabundance is determined for a M+1 signals.
 50. The device of claim 49,wherein the operation determining the relative abundance for a M+1signals further comprises, determining a maximum predicted count of anM+1 element based on an intensity of a putative M+1 signal, an intensityof a putative monoisotopic signal, and a terrestrial natural abundanceof the M+s1 element.
 51. The device of claim 50, wherein the M+1 elementis carbon, nitrogen, or silicon.
 52. The device of claim 48, wherein therelative abundance is determined for a M+2 signals.
 53. The device ofclaim 52, wherein the operation determining the relative abundance forM+2 signals further comprises, determining a maximum predicted count ofan M+2 element based on an intensity of a putative monoisotopic signal,an intensity of a putative M+2 signal and a terrestrial naturalabundance of the M+2 element.
 54. The device of claim 52, wherein theoperation determining the relative abundance for M+2 signals furthercomprises, determining a maximum predicted count of an M+2 element basedon an intensity of a putative monoisotopic signal, an intensity of aputative M+2 signal, an intensity of a putative M+4 signal and aterrestrial natural abundance of the M+2 element.
 55. The device ofclaim 52, wherein the operation determining the relative abundance forM+2 signals further comprises, determining a maximum predicted count ofan M+2 element based on an intensity of a putative monoisotopic signal,an intensity of a putative M+4 signal, an intensity of a putative M+6signal and a terrestrial natural abundance of the M+2 element.
 56. Thedevice of claim 52, wherein the operation determining the relativeabundance for M+2 signals further comprises, determining a maximumpredicted count of an M+2 element based on an intensity of a putativemonoisotopic signal, an intensity of a putative M+6 signal, an intensityof a putative M+8 signal and a terrestrial natural abundance of the M+2element.
 57. The device of claim 52, wherein the operation determiningthe relative abundance for M+2 signals further comprises: determining ifone or more analytes contain both chlorine and bromine; and if theanalytes contain both chlorine and bromine determining a maximumpredicted count of an M+2 element based on a terrestrial naturalabundance of ³⁷Chlorine, a terrestrial natural abundance of ⁸¹Bromine.